Task-Aware BERT-based Sentiment Analysis from Multiple Essences of the Text

Jia Hao Hsu, Chung Hsien Wu, Tsung Hsien Yang

研究成果: Conference contribution

摘要

Text sentiment analysis has always been an important topic in the research of human-computer interactions and is generally applied to help businesses monitor product satisfaction and understand customer needs. The research in this study intends to consider the sentiment of the with a focus on capturing multiple essences of the , such as words, events and sentence, in a specific task. First, an approach to automatically extract the task-specific emotional key terms in the corpus of a specific task is proposed. Task-specific key events are manually/automatically designed for the specific application task. The BERT -based model is employed to integrate the outputs from sentence, key terms and events of the input for task-aware sentiment analysis. The pre-trained sentence-based BERT model is fine-tuned using the Ren-CECps, a large-size Chinese weblog emotion corpus. Then we transfer the encoder weights to a new model and initialize a new linear layer, and finally fine-tune this model to fit the specific task. For evaluation, the Telecom Domain Customer Service Corpus (TD-CSC), a telecommunications service dataset, was used. The experimental results show that the proposed BERT -based model using multiple essences improved the correct rate by 10% compared to that without using the multiple essence features.

原文English
主出版物標題2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1982-1986
頁數5
ISBN(電子)9789881476890
出版狀態Published - 2021
事件2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan
持續時間: 2021 12月 142021 12月 17

出版系列

名字2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings

Conference

Conference2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
國家/地區Japan
城市Tokyo
期間21-12-1421-12-17

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦視覺和模式識別
  • 訊號處理
  • 儀器

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